Early fault alarm method of rolling bearing based on wavelet analysis and convolution neural network
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摘要:
针对航空发动机主轴承状态监测中存在的真实故障样本难以获取、变工况通用告警阈值难以界定以及早期微弱故障难以识别问题,提出一种滚动轴承早期故障通用告警方法。该方法仅基于正常样本训练卷积神经网络,依靠退化数据与正常数据间的特征距离来构造演化状态指示器,并基于训练标签实现不同工况数据告警阈值的统一,同时利用小波频带包络信号对早期高频故障的敏感性实现提前预警;然后,基于拉依达准则划分演化阶段,确定退化与失效阈值;最后基于粒子滤波对剩余寿命进行了逐步跟踪预测。3组试验结果证明,基于不同故障试验数据的小波分析和卷积神经网络(Wavelet-CNN)特征,其退化阈值与失效阈值能被归一化在0.6和1.0附近,且对退化开始时间的预测较非小波方法分别提前13.01%、12.33%及13.70%。
Abstract:In view of the problems in condition monitoring of aero-engine main bearing, such as the difficulty in obtaining the real fault samples, the limitation in defining the general alarm threshold under variable conditions and the difficulty to identify the early weak faults, a general alarm method for early faults of rolling bearings was proposed. This method only trained convolutional neural networks based on normal samples, constructed evolution state indicator by the characteristic distance between degraded data and normal data, and unified the alarm thresholds of different working conditions based on training labels; at the same time, the sensitivity of wavelet band envelope signal to early high-frequency fault was used to realize early warning; then, the evolution stages were divided based on the Pauta criterion, according to which the degradation and failure thresholds were determined; finally, the remaining useful life was predicted step by step based on particle filter. Three groups of test results showed that the degradation threshold and failure threshold of wavelet analysis and convolution neural network (Wavelet-CNN) based on different fault test data can be normalized around 0.6 and 1.0, and the predictions of degradation start time were 13.01%, 12.33% and 13.70% earlier than those of non wavelet methods respectively.
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Key words:
- rolling bearing /
- general diagnosis /
- early warning /
- deep learning /
- wavelet analysis
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表 1 CNN参数
Table 1. Parameters of CNN
项目 输入层 池化层 卷积层 池化层 卷积层 池化层 卷积层 全连接层 回归层 参数 64×32×1 2×2 3×3, 16 2×2 3×3, 32 2×2 3×3, 32 1 1 输出 64×32×1 32×16×1 32×16×16 16×8×16 16×8×32 8×4×32 8×4×32 1×1×1 1 表 2 3组滚动轴承全寿命试验数据集相关参数
Table 2. Related parameters of three groups of rolling bearing whole lifetime test datasets
数据集 轴承型号 转速/
(r/min)载荷/kN 采样率/
kHz采样周期/
min样本
长度试验
时长/h故障类型 IMS ZA 2115 2000 26.67 20 10 20480 163 外圈剥落 IDES HRB 6206 11500 6.25 32 6 65536 30 内圈剥落 XJTU-SY UER 204 2250 11 25.6 1 32768 8.9 保持架裂断 表 3 轴承主要参数
Table 3. Main parameters of bearings
轴承型号 滚珠直径/mm 节径/mm 滚珠数 接触角/(°) ZA 2115 8.4 71.5 16 15.17 HRB 6206 9.5 46 9 0 UER 204 7.9 34.6 8 0 表 4 基于拉依达准则的滚动轴承退化与失效时刻和阈值确定
Table 4. Determination of degradation and failure time and threshold of rolling bearings based on Pauta criterion
方法 ZA 2115全寿命
(984时刻)HRB 6206全寿命
(300时刻)UER 204全寿命
(533时刻)退化开始 失效开始 退化开始 失效开始 退化开始 失效开始 有效值 阈值 0.1061 0.1643 2.5012 4.1450 1.5452 2.9672 时刻 649 703 251 274 341 372 CNN特征值 阈值 0.7599 1.2234 0.6762 1.0962 0.7714 1.1806 时刻 675 703 249 279 194 315 Wavelet-CNN
特征值(d1)阈值 0.5981 1.0272 0.6420 1.0877 0.6700 0.9946 时刻 547 702 212 275 121 135 表 5 3组滚动轴承数据基于粒子滤波RUL预测结果误差对比
Table 5. Error comparison of RUL prediction results of three groups of rolling bearing data based on particle filter
数据集 特征类型 拟合曲线的
RMSE预测结果的
归一化RMSEZA 2115
(984点)有效值 0.0252 0.2468 CNN特征值 0.0177 0.1483 Wavelet-CNN
特征值0.0143 0.1314 HRB 6206
(300点)有效值 0.1088 0.2668 CNN特征值 0.0280 0.1085 Wavelet-CNN
特征值0.0629 0.1321 UER 204
(533点)有效值 0.2135 0.4118 CNN特征值 0.1466 0.1620 Wavelet-CNN
特征值0.0863 0.1335 -
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